title: Estimating personalized risk ranking using laboratory test and medical knowledge (UMLS).
abstract: In this paper, we introduce a Concept Graph Engine (CG-Engine) that generates patient specific personalized disease ranking based on the laboratory test data. CG-Engine uses the Unified Medical Language System database as medical knowledge base. The CG-Engine consists of two concepts namely, a concept graph and its attributes. The concept graph is a two level tree that starts at a laboratory test root node and ends at a disease node. The attributes of concept graph are: Relation types, Semantic types, Number of Sources and Symmetric Information between nodes. These attributes are used to compute the weight between laboratory tests and diseases. The personalized disease ranking is created by aggregating the weights of all the paths connecting between a particular disease and contributing abnormal laboratory tests. The clinical application of CG-Engine improves physician's throughput as it provides the snapshot view of abnormal laboratory tests as well as a personalized disease ranking.
authors.forename: Meru A
authors.initials: MA
authors.lastname: Patil
authors.forename: Sandip
authors.initials: S
authors.lastname: Bhaumik
authors.forename: Soubhik
authors.initials: S
authors.lastname: Paul
authors.forename: Swarupananda
authors.initials: S
authors.lastname: Bissoyi
authors.forename: Raj
authors.initials: R
authors.lastname: Roy
authors.forename: Seungwoo
authors.initials: S
authors.lastname: Ryu
mesh_terms: D000465:Algorithms; D001835:Body Weight; D016208:Databases, Factual; D003920:Diabetes Mellitus / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D006801:Humans; D008107:Liver Diseases / Q000175:diagnosis; D018570:Risk Assessment / Q000379:methods*; D012660:Semantics; D012984:Software; D017432:Unified Medical Language System